Analysis of Regional Surface CO2 Fluxes Using the MEGA Satellite Data Assimilation System
Name
remotesensing-17-03720-v2.pdf
Description
Published version
Size
3.16 MB
Format
Adobe PDF
Checksum (MD5)
b0a8952cfccec86f92ca6d36223f9ff1
Author(s)
Hu, Liting
Hu, Xiaoyi
Jiang, Fei
He, Wei
Deng, Zhu
Fang, Shuangxi
Fang, Xuekun
Date Issued
November 13, 2025
Journal
Remote Sensing
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Hu, L., Hu, X., Jiang, F., He, W., Deng, Z., Fang, S., & Fang, X. (2025). Analysis of Regional Surface CO2 Fluxes Using the MEGA Satellite Data Assimilation System. Remote Sensing, 17(22), 3720.
Version
Final published version
Abstract
Understanding the dynamics of terrestrial carbon sources and sinks is crucial for addressing climate change, yet significant uncertainties remain at regional scales. We developed the Monitoring and Evaluation of Greenhouse gAs Flux (MEGA) inversion system with satellite data assimilation and applied it to China using OCO-2 V11.1r XCO2 retrievals. Our results show that China’s terrestrial ecosystems acted as a carbon sink of 0.28 ± 0.15 PgC yr−1 during 2018–2023, consistent with other inversion estimates. Validation against surface CO2 flask measurements demonstrated significant improvement, with RMSE and MAE reduced by 30%–46% and 24–44%, respectively. Six sets of prior sensitivity experiments conclusively demonstrated the robustness of MEGA. In addition, this study is the first to systematically compare model-derived and observation-based background fields in satellite data assimilation. Ten sets of background sensitivity experiments revealed that model-based background fields exhibit superior capability in resolving seasonal flux dynamics, though their performance remains contingent on three key factors: (1) initial fields, (2) flux fields, and (3) flux masks (used to control regional flux switches). These findings highlight the potential for further refinement of the atmospheric inversion system.
MIT Department
Massachusetts Institute of Technology. Center for Global Change Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/rs17223720
